Research Seminar “Analytical Sociology and Big Data”

Bachelor 2020/2021

Type: Elective course (Sociology and Social Informatics)

Area of studies: Sociology

Delivered by: Department of Sociology

Where: Saint-Petersburg School of Social Sciences

When: 2 year, 1-4 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Daniil A. Alexandrov, Valeria A. Ivaniushina, Olesya Vólchenko

Language: English

ECTS credits: 4

Contact hours: 42

Full Syllabus

Abstract

The purpose of the course is to provide students with skills necessary for conducting social research based on big data analysis. During the course different features of analytical approach towards big data will be covered as well as a variety of examples of reports and articles relevant for the field.

Learning Objectives

be able to read and critically discuss articles from the field of the big data analysis and conduct empirical research using different sources of the data.

Expected Learning Outcomes

understand modern features and issues of big data analytics
learn basic methodological principles and major methods applicable for big data analysis
be able to apply the methods of analytical sociology and social statistics to the analysis of big data
use basic rules of statistical inference
employ major sociological concepts as instruments of sociological research
read and discuss journal articles and book chapters; participate in group research projects; give presentations on their research projects and topics of their interest

Course Contents

Introduction to analytical sociology and applications
Basic principles of analytical sociology. Key authors in the field of analytical sociology
Sources of big data; quality of data
Typology of data sources. Principles of data collection. Big data quality assessment
Literature review: basic principles and search for the articles
Basic principles. Logic of literature review. Sources of literature
Operationalization of theoretical concepts and measurement
Operationalization. Measurement principles in sociology
Research design for the big data analysis
Typology of research designs. Most common research designs for big data researches
Studying stratification and intergenerational mobility using big data
General idea of social stratification analysis. Big data sources. Example article
Social movements analysis using big data
General idea of social movements analysis. Big data sources. Example article
Educational research using big data
General idea of education research. Big data sources. Example article
Health research using big data
General idea of health research. Big data sources. Example article
Ethical issues of the big data research
General ethical principles. Ethical issues in big data research
Presentation of the research results
General principles of good presentation. Practical session

Assessment Elements

Participation in class discussions
In-class assignments
In-class assignments grade will be calculated as an average score for all types of written activities during the seminars.
Presentation of the individual project
Presentation of the individual project includes final presentation on the topic of student’s course work and should represent a solid presentation of research framework, literature review, data description, data analysis and main conclusions.

Interim Assessment

Interim assessment (4 module)
0.3 * In-class assignments + 0.4 * Participation in class discussions + 0.3 * Presentation of the individual project

Bibliography

Recommended Core Bibliography

Van Rijmenam, M. (2014). Think Bigger : Developing a Successful Big Data Strategy for Your Business. New York: AMACOM. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=686831

Recommended Additional Bibliography

Manzo, G. (2014). Analytical Sociology : Actions and Networks. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=714658

Course Syllabus